const PhoneRuntime = {
  _loadedModels: new Map(),
  _runtimeType: 'mock',

  async initializeRuntime() {
    this._detectRuntime();
    log('Runtime: ' + this._runtimeType);
  },

  _detectRuntime() {
    if (typeof navigator !== 'undefined' && navigator.gpu) {
      this._runtimeType = 'webgpu';
    } else if (typeof WebAssembly !== 'undefined') {
      this._runtimeType = 'wasm';
    } else {
      this._runtimeType = 'mock';
    }
    // Native CoreML/MLX only available in native iOS app, not Safari
  },

  selectBestRuntime() {
    return this._runtimeType;
  },

  async loadTextEmbeddingModel(modelId) {
    // v1: mock load; v2 would use transformers.js or ONNX Runtime Web
    this._loadedModels.set(modelId || 'text-embedding', { type: 'embedding', loaded: true });
    log('Loaded embedding model (mock)');
    return true;
  },

  async loadImageModel(modelId) {
    this._loadedModels.set(modelId || 'image-classifier', { type: 'image', loaded: true });
    log('Loaded image model (mock)');
    return true;
  },

  async loadRedactionModel(modelId) {
    this._loadedModels.set(modelId || 'privacy-redact', { type: 'redaction', loaded: true });
    log('Loaded redaction model (mock)');
    return true;
  },

  isModelLoaded(modelId) {
    return this._loadedModels.has(modelId);
  },

  async runTextEmbedding(text) {
    if (!this._loadedModels.has('text-embedding')) {
      await this.loadTextEmbeddingModel();
    }
    // v1: return deterministic mock embedding vector
    const vec = new Array(384).fill(0);
    for (let i = 0; i < text.length && i < 384; i++) {
      vec[i] = (text.charCodeAt(i) % 100) / 100;
    }
    return { embedding: vec, model: 'mock-embedding', runtime: this._runtimeType };
  },

  async runImageClassification(imageDataUrl) {
    if (!this._loadedModels.has('image-classifier')) {
      await this.loadImageModel();
    }
    // v1: mock classification based on image size
    const mockLabels = ['cat', 'dog', 'bird', 'car', 'tree'];
    const idx = (imageDataUrl.length % mockLabels.length);
    return {
      labels: [
        { label: mockLabels[idx], score: 0.92 },
        { label: mockLabels[(idx + 1) % mockLabels.length], score: 0.05 },
      ],
      model: 'mock-image',
      runtime: this._runtimeType,
    };
  },

  async runPrivacyRedaction(text) {
    if (!this._loadedModels.has('privacy-redact')) {
      await this.loadRedactionModel();
    }
    // Simple regex-based redaction for demo
    const redacted = text
      .replace(/\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/g, '[EMAIL]')
      .replace(/\b\d{3}-\d{2}-\d{4}\b/g, '[SSN]')
      .replace(/\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/g, '[CARD]');
    return { redacted, entities_removed: text !== redacted, model: 'mock-redact', runtime: this._runtimeType };
  },

  async hashModelWeights() {
    return 'mock-model-hash-' + this._runtimeType;
  },

  getRuntimeStatus() {
    return {
      type: this._runtimeType,
      models_loaded: Array.from(this._loadedModels.keys()),
      webgpu: !!navigator.gpu,
      wasm: typeof WebAssembly !== 'undefined',
    };
  }
};